Dear all, I am bit new to the python/pyplot. This might be simple, but I guess I am missing something here. I have data file as follows:
2.1576318858 -1.8651195165 4.2333428278 2.1681875208 -1.9229968780 4.1989176884 2.3387636157 -2.0376253255 2.4460899122 2.1696565965 -2.6186941271 4.4172007912 2.0848862071 -2.1708981985 3.3404520962 2.0824347942 -1.9142798955 3.3629290206 2.0281685821 -1.8103363482 2.5446721669 2.3309993378 -1.8721153619 2.7006893016 2.0957461483 -1.5379071451 4.5228264441 2.2761376261 -2.5935979811 3.9231744717 . . . (total of 200 lines) Columns 1,2,3 corresponds to x,y,z axis data points. This is not a continuous data. I wish to make a plot as a 2D with 3rd dimension (i.e z-axis data) as a color map with color bar on right hand side. As a beginner, I tried to follow tutorial with some modification as follows: http://matplotlib.org/examples/pylab_examples/tricontour_vs_griddata.html # Read data from file: fl1 = open('flooding-psiphi.dat','r').readlines() xs = ys = zs = [] for line in fl1: line = line.split() xs.append(float(line[0])) ys.append(float(line[1])) zs.append(float(line[2])) print xs[0], ys[0], zs[0] xi = np.mgrid[-5.0:5.0:200j] yi = np.mgrid[-5.0:5.0:200j] zi = griddata((x, y), z, (xi, yi), method='cubic') plt.subplot(221) plt.contour(xi, yi, zi, 15, linewidths=0.5, colors='k') plt.contourf(xi, yi, zi, 15, cmap=plt.cm.rainbow, norm=plt.Normalize(vmax=abs(zi).max(), vmin=-abs(zi).max())) plt.colorbar() # draw colorbar plt.plot(x, y, 'ko', ms=3) plt.xlim(-5, 5) plt.ylim(-5, 5) plt.title('griddata and contour (%d points, %d grid points)' % (npts, ngridx*ngridy)) #print ('griddata and contour seconds: %f' % (time.clock() - start)) plt.gcf().set_size_inches(6, 6) plt.show() However, I failed and getting long error as follows: QH6154 qhull precision error: initial facet 1 is coplanar with the interior point ERRONEOUS FACET: - f1 - flags: bottom simplicial upperDelaunay flipped - normal: 0.7071 -0.7071 0 - offset: -0 - vertices: p600(v2) p452(v1) p304(v0) - neighboring facets: f2 f3 f4 While executing: | qhull d Qz Qbb Qt Options selected for Qhull 2010.1 2010/01/14: run-id 1531309415 delaunay Qz-infinity-point Qbbound-last Qtriangulate _pre-merge _zero-centrum Pgood _max-width 8.8 Error-roundoff 1.2e-14 _one-merge 8.6e-14 _near-inside 4.3e-13 Visible-distance 2.5e-14 U-coplanar-distance 2.5e-14 Width-outside 4.9e-14 _wide-facet 1.5e-13 precision problems (corrected unless 'Q0' or an error) 2 flipped facets The input to qhull appears to be less than 3 dimensional, or a computation has overflowed. Qhull could not construct a clearly convex simplex from points: - p228(v3): 2.4 2.4 1.4 - p600(v2): 1.4 1.4 8.8 - p452(v1): 5.7 5.7 8 - p304(v0): -3.1 -3.1 2.4 The center point is coplanar with a facet, or a vertex is coplanar with a neighboring facet. The maximum round off error for computing distances is 1.2e-14. The center point, facets and distances to the center point are as follows: center point 1.595 1.595 5.173 facet p600 p452 p304 distance= 0 facet p228 p452 p304 distance= 0 facet p228 p600 p304 distance= 0 facet p228 p600 p452 distance= 0 These points either have a maximum or minimum x-coordinate, or they maximize the determinant for k coordinates. Trial points are first selected from points that maximize a coordinate. The min and max coordinates for each dimension are: 0: -3.134 5.701 difference= 8.835 1: -3.134 5.701 difference= 8.835 2: -2.118e-22 8.835 difference= 8.835 If the input should be full dimensional, you have several options that may determine an initial simplex: - use 'QJ' to joggle the input and make it full dimensional - use 'QbB' to scale the points to the unit cube - use 'QR0' to randomly rotate the input for different maximum points - use 'Qs' to search all points for the initial simplex - use 'En' to specify a maximum roundoff error less than 1.2e-14. - trace execution with 'T3' to see the determinant for each point. If the input is lower dimensional: - use 'QJ' to joggle the input and make it full dimensional - use 'Qbk:0Bk:0' to delete coordinate k from the input. You should pick the coordinate with the least range. The hull will have the correct topology. - determine the flat containing the points, rotate the points into a coordinate plane, and delete the other coordinates. - add one or more points to make the input full dimensional. Traceback (most recent call last): File "./scatter.py", line 43, in <module> zi = griddata((x, y), z, (xi, yi), method='linear') File "/usr/lib/python2.7/dist-packages/scipy/interpolate/ndgriddata.py", line 183, in griddata ip = LinearNDInterpolator(points, values, fill_value=fill_value) File "interpnd.pyx", line 192, in scipy.interpolate.interpnd.LinearNDInterpolator.__init__ (scipy/interpolate/interpnd.c:2598) File "qhull.pyx", line 948, in scipy.spatial.qhull.Delaunay.__init__ (scipy/spatial/qhull.c:4121) File "qhull.pyx", line 172, in scipy.spatial.qhull._construct_delaunay (scipy/spatial/qhull.c:1314) RuntimeError: Qhull error Could anyone help me to point out what exactly I am missing here. I just wish to plot 2D map with color bar for Z-axis. Thank you in advance. -- DJ
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